全球領先數碼旅遊平台之一的 Booking.com 揭曉 2025 年香港最新的旅遊趨勢預測，預期許多旅客將重新定義他們體驗及與世界互動的方式。Booking.com 將繼續透過 Connected Trip 的願景，讓旅客於平台上輕鬆預訂一切旅遊所需，包括機票、住宿、租車和精選體驗，享受愉快且無壓力的旅程。 在 2025 年，旅客對旅遊將變得更深思熟慮，因為他們將有全新目的去探索世界:旅客不再滿足於千篇一律的傳統體驗，將利用旅程來改變自己、與他人的關係，以及周遭的世界。未來的旅遊趨勢將著重於可持續發展、身心健康和文化體驗。 i. 夜間旅遊(Noctourism) 約三分之二(71%) 的香港旅客希望可以逃離白天的擠擁，前往較漆黑的目的地，享受 午夜的魅力，比全球旅客比例高出近 10%。氣候變化是其中一項影響因素，不少旅客計劃減少在陽光下的時間(66%)，或計劃在晚上和清晨期間活動(64%)。旅客對夜間活動興趣的同時，也加深了與大自然的聯繫，過半旅客(55%)選擇預訂無燈光住宿，以減少光污染和保護動植物。 ii. 跨世代家庭同遊 由於文化上的影響，跨世代旅遊在香港逐漸流行，並在 2025 年以更「無私」的方式呈現 –「SKI」假期(Spending Kids’ Inheritance — 花掉孩子的遺產)。相比起資產的傳承，現時長輩們會優先考慮一起享受生活，投資於家庭同遊體驗，例如為年輕一代支付旅遊費用， 以加強聯繫和創造美好時光。大部分嬰兒潮一代願意贊助子女 (86%) 和孫兒 (62%) 的旅費。 iii. AI = 另類行程(Alternative Itineraries) 隨著人工智能和科技的進步，63%的香港旅客會善用科技，作出更明智決擇和尋找更地道的體驗。 Booking.com 的 AI Trip Planner 旨在為旅客提供度身訂造和省時的旅遊方案，以便打造另類行程。超過一半的香港旅客 (54%) 表示有興趣使用 AI 來規劃更深入當地和社區的行程。與此同時，旅客亦考慮以負責任的態度來應用科技，例如 36% 的旅客在遊覽鮮為人知的目的地時，不會在社交媒體上標記位置，以免大批網民蜂擁而至。因此，科技在積極 協助尋找替代方案，讓旅客在分享旅遊體驗的同時，不會讓熱門景點超負荷。 iv. 復古旅遊 受到成本和氣候意識的影響，時尚旅客正邁向復古之旅，享受節儉的旅行。逾半 (60%) 的香港旅客表示，他們有興趣在旅行期間才購買度假服裝，特別是 Z 世代(64%)。另外， 47% 的旅客會在出遊時逛二手店，近四分之三 (71%)的旅客曾在國外購買復古或二手 商品。 58% 的香港旅客打算在旅行時更節儉，而 65% 的旅客將更嚴格規劃預算，以最大限度地 提升旅遊體驗，因此，在二手店尋找價廉物美的寶物已成為行程不可或缺的部分。旅客更喜 歡將文化聯繫帶返家鄉，在購物的同時，作出環保和省錢的選擇。 v. 養生休閒之旅 養生旅遊近年盛行，而這趨勢在 2025 年將進一步受到追捧。近半的香港旅客(49%) 對 延年益壽的養生之旅感到興趣，他們不只追求透過旅行短暫地恢復活力，而是希望達至更長壽和更健康的生活。58% 的旅客願意為延長壽命和增加幸福感的旅遊而付費。 深度煥活是旅程的重中之重，包括振動身體療程(56%) 、紅光療法 (54%)、冷凍療 法 (53%)和幹細胞治療 (50%)。超過三分之二 (74%) 的旅客正尋找可融入日常生活 的養生活動，包括學習定時喝咖啡(49%)和靜脈注射療法 (45%)，以重獲身心平衡。 透過 Booking.com 體驗無縫旅程 Booking.com 將一如以往為旅客提供無與倫比的便利和輕鬆的旅行體驗，以滿足旅客行為和需求的變化。 Booking.com 的 Connected Trip 理念一直致力於聯繫旅客和他們理想的住宿，同時為他們提供滿足2025 年所有旅遊趨勢、需求和預測的全面解決方案。旅客可在 Booking.com 上找到超過 300 項於香港的體驗，例如香港維多利亞港的晚間遊輪和 Big Bus 香港夜遊，非常適合尋求獨特夜間體驗的旅客。Booking.com 將繼續提供優質的整體預訂體驗，讓旅客在平台上更輕鬆地規劃和預訂旅程。 Booking.com 台灣及香港區域經理詹雅伃女士表示:「旅遊業不斷持續發展，我們看到旅客更願 意打破常規。希望在探索世界的同時，與自身、親友和周遭環境建立更深層次的聯繫，此趨勢將於 2025 年變得更普及。Booking.com 作為領先的全球數碼旅遊平台，將繼續帶領業界去了解旅客不斷改變的需求，為每位香港旅客提供無縫且個人化的服務去體驗世界各地的美好，讓旅遊變得更容易。」 LinkedIn Email Facebook Twitter WhatsApp source

RPA stands for robotic process automation. It represents some of the most cutting-edge technological developments of the modern era with its ability to improve efficiency gains in business operations. RPA uses software bots to automate tasks, eliminating the need for people to perform manual labor and other tasks that involve rote, repetitive processes. RPA is deployed in many IT settings and is ideally suited for contact centers, which are intensely customer service-focused environments. 1 RingCentral RingEx Employees per Company Size Micro (0-49), Small (50-249), Medium (250-999), Large (1,000-4,999), Enterprise (5,000+) Medium (250-999 Employees), Large (1,000-4,999 Employees), Enterprise (5,000+ Employees) Medium, Large, Enterprise Features Hosted PBX, Managed PBX, Remote User Ability, and more 2 Talkroute Employees per Company Size Micro (0-49), Small (50-249), Medium (250-999), Large (1,000-4,999), Enterprise (5,000+) Any Company Size Any Company Size Features Call Management/Monitoring, Call Routing, Mobile Capabilities, and more The clear case for RPA in contact centers RPA bots act in place of human operators, having first gained prominence in the manufacturing industry where low-skilled, labor-intensive tasks were highly prevalent. The introduction of RPA bots was not necessarily to replace human labor, but to displace and reallocate it for more productive endeavors. In places like contact centers, for instance, they do a lot of the repetitive and boring tasks so that human agents are free to focus on more creative, value-adding tasks. Typically, RPA bots work using an API, but they can also function and interact at a graphical user interface layer to execute complex workflows. While RPA accelerates productivity, not all tasks, processes, or environments are ideal for process automation. RPA is primarily used for the following: Tasks with standardized processes and functions that are predominately rule-based. Mundane tasks that are labor-intensive and time-consuming. Jobs that operate in reliable, data-rich, and data-driven environments. Workplaces that process high-volume, monotonous tasks and need consistent handling without experiencing diminishing returns. Business processes that use well-defined, standardized data sets that are easy to structure and categorize. Tasks that deal with a large volume of digitized data that’s adequately readable. After selecting the right vendor, deploying robotic process automation requires capturing the steps you want to automate, executing the pilot process with your preferred vendor, and then implementing it. When a contact center deploys one or many automated bots, it allows the center to scale its operations while delivering high-volume processes swiftly, accurately, and consistently without a downgrade in quality or efficiency. Moreover, RPA bots provide these benefits at significant cost savings compared to human agents who would otherwise be wasted on menial tasks. Ultimately, they allow human capital to focus on tasks requiring intuitive judgment. For contact center employees specifically, RPA can automate numerous workflow tasks that are part of a typical workday. These tasks rarely have an impact on customer satisfaction outcomes, so contact center employees benefit from having more time to focus on activities that are more productive. Additionally, RPA technology can also streamline certain tasks and fortify data security practices for the company, taking some of that burden off the employees. At the same time, RPA bots don’t need to take bathroom breaks and can work all day long without experiencing diminishing returns. Unattended and attended bots For the most part, contact centers use unattended bots that execute processes behind the scenes. These unattended RPA bots are primarily provisioned to tackle rule-based processes automatically, which allows them to automate back-office services at scale. Meanwhile, attended RPA bots require human intervention and/or instruction to perform tasks, as they typically do things that depend on the knowledge and expertise of a contact center agent. For instance, an attended bot can act as a virtual assistant that is manually triggered to gather customer information while the agent interacts with a customer. Furthermore, an assisted RPA bot can even take the information it gathers from an agent’s computer and fill in forms with personalized customer details during a call. This makes RPA bots especially useful for agents who deal with various support chats, Voice over Internet Protocol (VoIP) calls, and other routine data input processes. The top opportunities for contact center RPA Contact centers are filled with repetitive, time-consuming tasks that can drain efficiency and hinder customer satisfaction. RPA offers a powerful way to streamline these processes,  reduce errors from manual entry, and free up agents to focus on more valuable interactions. Here are eight key opportunities where RPA can drive significant impact and transform operations 1. Enhanced customer verification While traditional IVR systems are effective for basic customer authentication — such as verifying account numbers or PINs — RPA goes beyond simple queries to handle more complex, dynamic verification processes. For example, RPA bots can: Pre-validate customer information: Pull and cross-check data from multiple systems, such as CRM platforms and payment records, to ensure accuracy before escalating to an agent. Handle conditional logic: Adapt verification steps based on the caller’s issue or account status. For example, if a payment dispute is flagged, RPA can pre-authorize verification layers like confirming recent transactions or linking a verified email. Initiate advanced authorization: Request sensitive approvals, such as confirming account changes or processing refunds, without requiring the customer to repeat details to a live agent. Beyond traditional identity verification, RPA also supports tasks like appointment confirmations. For businesses offering in-home services, RPA bots can proactively reach out to customers, verify service windows, and update scheduling systems — all without agent intervention. This enhanced approach saves time, reduces friction for customers, and ensures agents are equipped with verified, up-to-date information when they step in to assist. 2. Automated self-service Contact centers are often flooded with basic customer inquiries, like asking about product returns or how to file a warranty claim. These tasks don’t need a live agent, so they’re great candidates for automation. A simple IVR phone tree can handle simple tasks, like pressing a number to check your account balance. However, IVR is limited in what it can do — if a customer needs to update their billing information, manage a return,

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Proving its intention to support a wide range of enterprise use cases — including those that don’t require expensive, resource-intensive large language models (LLMs) — AI startup Cohere has released Command R7B, the smallest and fastest in its R model series.  Command R7B is built to support fast prototyping and iteration and uses retrieval-augmented generation (RAG) to improve its accuracy. The model features a context length of 128K and supports 23 languages. It outperforms others in its class of open-weights models — Google’s Gemma, Meta’s Llama, Mistral’s Ministral — in tasks including math and coding, Cohere says. “The model is designed for developers and businesses that need to optimize for the speed, cost-performance and compute resources of their use cases,” Cohere cofounder and CEO Aidan Gomez wrote in a blog post announcing the new model. Outperforming competitors in math, coding, RAG Cohere has been focused on enterprises and their unique use cases. The company introduced Command R in March and the powerful Command R+ in April, and has made upgrades throughout the year to support speed and efficiency. It teased Command R7B as the “final” model in its R series, and said it will release model weights to the AI research community. Cohere noted that a critical area of focus when developing Command R7B was to improve performance on math, reasoning, code and translation. The company appears to have succeeded in those areas, with the new smaller model topping the HuggingFace Open LLM Leaderboard against similarly-sized open-weight models including Gemma 2 9B, Ministral 8B and Llama 3.1 8B.  Further, the smallest model in the R series outperforms competing models in areas including AI agents, tool use and RAG, which helps improve accuracy by grounding model outputs in external data. Cohere said Command R7B excels at conversational tasks including tech workplace and enterprise risk management (ERM) assistance; technical facts; media workplace and customer service support; HR FAQs; and summarization. Cohere also stated that the model is “exceptionally good” at retrieving and manipulating numerical information in financial settings. All told, Command R7B ranked first, on average, in important benchmarks including instruction-following evaluation (IFeval); big bench hard (BBH); graduate-level Google-proof Q&A (GPQA); multi-step soft reasoning (MuSR); and massive multitask language understanding (MMLU).  Removing unnecessary call functions Command R7B can use tools including search engines, APIs and vector databases to expand its functionality. Cohere reports that the model’s tool use performs strongly against competitors in the Berkeley Function-Calling Leaderboard, which evaluates a model’s accuracy in function calling (connecting to external data and systems).  Gomez pointed out that this proves the model’s effectiveness in “real-world, diverse and dynamic environments” and removes the need for unnecessary call functions. This can make it a good choice for building “fast and capable” AI agents. For instance, Cohere pointed out, when functioning as an internet-augmented search agent, Command R7B can break complex questions down into subgoals, while also performing well at advanced reasoning and information retrieval. Because it is small, Command R7B can be deployed on lower-end and consumer CPUs, GPUs and MacBooks, allowing for on-device inference. The model is available now on the Cohere platform and HuggingFace. Pricing is $0.0375 per one million input tokens and $0.15 per one million output tokens. “It is an ideal choice for enterprises looking for a cost-efficient model grounded in their internal documents and data,” wrote Gomez.  source

Amazon Web Services (AWS) may have been caught off guard by the sudden rise of generative AI (genAI), but now it’s coming for the competition from every possible angle. That was the message from the opening moments of CEO Matt Garman’s keynote at AWS re:Invent 2024, which reminded the audience of AWS’s breadth of services, with a security-centric pitch that set the stage for everything that came after. There was the usual blizzard of announcements, big and small (more on those below). The team of Forrester analysts onsite and beyond identified these key takeaways: The second round of cloud AI competition has begun. As genAI early adopters contemplate scale-out strategies, AWS has a message for both long-standing customers and prospects reeling from VMware price increases: We can make genAI work with the data and juiced-up versions of services that customers already have. Amazon CEO and ex-AWS boss Andy Jassy took the keynote stage to announce Nova, a new set of models. For sheer power, AWS is building a new super cluster for AI training for its AI partner, Anthropic, the recent recipient of $4 billion in AWS investment, using AWS’s proprietary Trainium chips as a work-around for NVIDIA’s GPU dominance. Meanwhile, the Bedrock managed AI service will serve as a marketplace for AI models. Mainstream AI service adoption will be abstracted and serverless. The keynote by Swami Sivasubramanian, vice president of AI and data at AWS, rolled out a series of closely intertwined enhancements to existing services like SageMaker and Kendra to tackle genAI challenges such as retrieval-augmented generation (RAG), a counter to Microsoft and Google’s top-to-bottom AI cloud solutions. AWS also pushed Amazon Q as an all-purpose generative AI assistant, with more third-party integrations, expanded development language support, and natural language automation for everything from data AI readiness to modernizing workflows. AWS doubles down on data and storage for enterprise AI. AWS understands that its customers’ data has gravity — and wants to entice them to add more. The company showcased table buckets and queryable metadata updates to S3 that make it an ideal platform for data lakehouse architectures, especially with SageMaker for AI application development. Other updates include FSx for Lustre intelligent tiering and new storage-focused instances with high-speed Nitro SSDs for modern AI applications. Related announcements included federated data strategy with AWS Clean Rooms and a physical data transfer terminal service. Here’s our take on key news from AWS re:Invent by category: AI. AWS continues to augment its AI services across the full lifecycle of genAI. The new Nova foundation model series includes four for languages and two for computer vision. As Forrester predicted in its Predictions 2025 report for cloud computing, AWS announced RAG capabilities, including structured data retrieval, GraphRAG, and Kendra GenAI Index for enterprise data. This broad-spectrum approach includes multiagent collaboration, Bedrock model distillation, automated reasoning, intelligent prompt routing, and multimodal toxicity detection. Data and analytics. AWS pushed the boundaries of data infrastructure with Aurora DSQL, a distributed and scalable SQL database. SageMaker got a boost with Unified Studio for integrated environments and HyperPod for orchestration and governance of model training, fine-tuning, and inferencing. Partners play a role via SageMaker third-party apps and Bedrock Marketplace. Infrastructure. The common theme here is enablement for AI, HPC, and database workloads, with AWS Trainium2 and NVIDIA H200 GPU options and storage optimization in the spotlight. The announced P5en instances with NVIDIA H200 GPUs include third-generation Elastic Fabric Adapters to reduce latency. New storage services include optimizations for analytics and autotiered file storage plus support for Pure and NetApp storage, apparently aimed at VMware migration. Application development. AWS continues to push its “well-architected” philosophy into its stack of cloud-native development and integration capabilities. Enterprises modernizing applications will be able to use services such as Step Functions and EventBridge to orchestrate workflows and connect resources across VPC and AWS account boundaries, easing integration of on-premises legacy apps. Security. AWS initially focused on the security of the cloud, relying on partners to provide the security in the cloud. Today, the AWS security portfolio is much broader. The newly enhanced GuardDuty will help users walk through the MITRE ATT&CK chain, while various AI-oriented security announcements focused on data lineages. AWS made further accommodations for securing multicloud environments, too. Sovereign cloud. AWS emphasized the launch of the European Sovereign Cloud, planned for Q4 2025 and backed by €7.8 billion in investment. This allows AWS to offer a single-provider multicloud environment in Europe. All cloud regions are powered by the secure Nitro hardware; pricing was not disclosed. Cloud sustainability. Power usage effectiveness (PUE) value of AWS data centers has been decreasing, and the announced new data center design is aimed at bringing data centers’ PUE below the market current average. AWS expects the new data center design to translate into a 14% reduction in carbon intensity, a 46% reduction in mechanical energy used, and 35% less embodied carbon. From a silicon standpoint, Inferentia2 now delivers up to 50% better performance per watt than its previous generation while Trainium2 is three times more energy-efficient than Trainium1. Cloud cost management/FinOps. AWS announced a slew of new capabilities, including adding genAI-enabled cost search function with Amazon Q Developer for chatbot-powered cost analysis, deeper anomaly detection with root-cause analysis, and a more accurate AWS Pricing Calculator that can ingest commitment purchases. AWS continues to lead the market in native cloud cost management, though competitor Microsoft Azure is close behind. SAP deployment. AWS and SAP announced GROW with SAP on AWS, enabling rapid deployment of SAP’s ERP solution with AWS’s cloud benefits. This collaboration simplifies the adoption of SAP S/4HANA Cloud Public Edition and introduces new AI-assisted innovations. Customers will benefit from unified billing, existing AWS credits, and enhanced performance with AWS Nitro and Graviton ARM chips. Network performance observability. A long-standing item on many network engineers’ wish lists is finally here: a holistic correlation of cloud networking performance and end-user experience. The CloudWatch Network Monitor solution will monitor network performance between AWS compute instances using flow

LogRhythm NextGen SIEM and SolarWinds Security Events Manager provide security information and event management tools to users who wish to ensure their organizational networks’ security and digital devices’ security. While both products provide SIEM capabilities, based on my analysis, I believe that each platform is optimized for different audiences: LogRhythm: Best for mature companies with deep security needs and a dedicated security operations center team. SolarWinds: Best for smaller teams or those looking for ease of reporting. 1 ManageEngine Log360 Employees per Company Size Micro (0-49), Small (50-249), Medium (250-999), Large (1,000-4,999), Enterprise (5,000+) Micro (0-49 Employees), Small (50-249 Employees), Medium (250-999 Employees), Large (1,000-4,999 Employees), Enterprise (5,000+ Employees) Micro, Small, Medium, Large, Enterprise Features Activity Monitoring, Blacklisting, Dashboard, and more 2 Graylog Employees per Company Size Micro (0-49), Small (50-249), Medium (250-999), Large (1,000-4,999), Enterprise (5,000+) Medium (250-999 Employees), Large (1,000-4,999 Employees), Enterprise (5,000+ Employees) Medium, Large, Enterprise Features Activity Monitoring, Dashboard, Notifications LogRhythm vs SolarWinds: Comparison table Features LogRhythm SolarWinds Pricing Contact vendor Starts at $2,992 Free trial No Yes Real-time monitoring Yes Yes Logging Yes Yes Analytics Yes Yes Reporting Yes Yes Threat management Yes Yes Incident response Yes Yes Customization Yes Yes Visit LogRhythm Visit SolarWinds Pricing LogRhythm LogRhythm offers perpetual licensing and subscription-based pricing plans, but the company doesn’t publicly disclose pricing information. I found the lack of pricing information disappointing, especially since LogRhythm doesn’t offer a free trial either. The licensing allows unlimited users and log sources, and can be run via the cloud, hardware, and virtual machines. To get an exact quote on pricing, contact LogRhythm. For more information, check out our LogRhythm vs Splunk comparison and our guide to adopting Splunk’s SIEM platform. SEE: Everything You Need to Know about the Malvertising Cybersecurity Threat (TechRepublic Premium) SolarWinds SolarWinds price starts at $2,992, with an option to get a custom pricing plan. Users can choose from the perpetual licensing option, which allows for indefinite license use, or the subscription-based model. While the cost of subscription-based licensing is initially far less than the cost of purchasing the perpetual license, the long-term cost is higher. A 30-day free trial is available from SolarWinds, which stood out to me since LogRhythm does not offer a free trial. LogRhythm vs SolarWinds: Feature comparison Threat monitoring LogRhythm monitors the data and events of organizations to detect anomalies throughout their networks and endpoints. The system collects security data, log data, and flow data to provide holistic real-time visibility and effective threat detection. The risk-based monitoring eliminates blind spots and identifies threats quickly, so users can respond to them before they cause severe damage. LogRhythm’s Endpoint Threat Detection Module uses threat intelligence, machine learning, and behavior analytics to find potential threats. I also appreciated that LogRhythm SIEM features multiple methods for threat detection, including identifying abnormal communication patterns, lateral movement, and changes to sensitive files. LogRhythm’s dashboards in action. Image: LogRhythm The SolarWinds SIEM solution provides continuous threat detection and real-time monitoring across users’ devices, services, files, and folders with its on-premises and multicloud deployments. Its intuitive dashboard and user interface make navigating the tool’s features easy for users. The centralized repository collects log data with the SIEM log collector tool, and raw network log data is organized and normalized for users in the system. This is one of the main reasons we named it the best choice for log aggregation on our list of the best SIEM tools. Additionally, I appreciate that SolarWinds features event-time correlation and advanced search capabilities, which are beneficial when conducting forensic analysis and security investigation. Threat analytics The LogRhythm NextGen SIEM platform uses multidimensional analytics to detect and stop security threats. Data collected by the system is normalized and correlated to identify potentially dangerous activity, which provides more accuracy. I also liked that network traffic and packet data are analyzed for patterns and behavioral outliers. The behavioral analysis features can process users’ activity within a network and identify deviations from normal baseline behavior; this is made possible through machine learning and can help ensure security from insider access abuse and data exfiltration. Additionally, the system allows for both contextual and unstructured searches. LogRhythm’s behavioral analysis features. Image: LogRhythm SolarWinds SIEM processes data and events for signs of security threats. The event log analyzer collects and analyzes log data, providing users insight with real-time visibility and context. Events are monitored to identify suspicious activity, such as permission changes and data modification. This data is then correlated through built-in and custom event correlation rules. I appreciate the automated insights offered by these SolarWinds features, which can be beneficial in helping users and network administrators diagnose system vulnerabilities, troubleshoot network problems, and improve their resource management. SolarWinds threat intelligence dashboard is a breeze to use. Image: SolarWinds Notifications When a threat is detected, the LogRhythm SIEM platform notifies its users based on their settings and the event’s severity. The Alarming and Response Manager can notify users when threats are detected or alert them of suspicious activity. The LogRhythm DetectX solution uses analytics to determine the prioritization of threats based on their severity level. I also liked that the security analytics can be customized, or entirely developed by users, so that no notifications slip through the cracks. In addition, users can integrate their tools with open-source or STIX/TAXII-compliant providers for even more alert precision. SolarWinds lets users set custom alerts or view SEM alert feeds, so they are always aware of security threats. Users can manage their systems to provide threshold-based alarms and notifications for security system event stream triggers, system errors, IDS/IPS systems with infection symptoms, crash reports, etc. I was also glad to see that its fine-tuned file integrity monitoring filters can be adjusted to ensure that only high-priority, file-related events create reports. When security events occur, or threats are identified, SolarWinds Log & Event Manager can send users notifications via email. Automation and response LogRhythm SIEM monitors organizational data and events for suspicious activity and takes actions to minimize the impact with its automated response

Are you tired of storing your photos in the cloud only to have the cloud provider change the deal and ask you to pray they don’t alter it further? Have you looked at all the photos on your hard drive or phone and thought, “Well, I have them. I just want easier ways to share and store them?” Self-hosting is for you. Here are five self-hosted photo storage options for you to consider. SEE: The definition of open source can be controversial, with some commercial models claiming the title. NextCloud This open-source project offers a mobile app and auto-upload feature. You can also use it as the host for other photo management apps. Plus, it can host non-photos like documents, calendars, and contacts. Nextcloud offers collaboration tools and file hosting. Screenshot: Megan Crouse / TechRepublic Photoprism This open-source app uses Google TensorFlow to automatically classify images. It extracts location data, detects duplicates, and can sync with Nextcloud. Photoprism offers smartphone-like smart search on an independent platform. Screenshot: Megan Crouse /TechRepublic Piwigo This open-source photo gallery software offers geolookup and multi-user support. Its album features are great, including batch management, album hierarchies, and more. It also has a mobile app. Piwigo is suitable for organizations and features admin tools and accounts for individuals. Screenshot: Megan Crouse / TechRepublic Lychee This open-source photo gallery app has excellent album and metadata-editing features. But, of course, its gallery features are where it does its best work. Just be aware that it doesn’t have any machine learning for auto-detection. Find Lychee on GitHub and run it on a private server. Screenshot: Megan Crouse / TechRepublic LibrePhotos Like most tools named Libre, LibrePhotos is a fork. In this case, it’s a fork of OwnPhotos that enables object detection, face training, and event-based album generation. LibrePhotos also integrates with NextCloud. LibrePhotos can be installed using Docker. Screenshot: Megan Crouse / TechRepublic Self-hosted photo storage provides albums, sharing, and automatic facial recognition — all the tools you’d get from that cloud provider that suddenly wants to charge you while still gathering your data for its ad targeting. If you have the space on your computer or server, you can self-host your own photos in no time. What is a self-hosted photo and video gallery? A self-hosted photo and video gallery allows users to store and share media on their own hardware or virtual server. It provides an alternative to smartphone photo apps or social media. What are the system requirements for hosting my photo gallery? Different self-hosted photo galleries have different requirements. Most services will have documentation available. Nextcloud, for instance, offers cloud hosting as part of some of its plans. Photo hosting generally doesn’t require a lot of RAM — PhotoPrism can be installed on a Raspberry Pi 4 with 4 GB of RAM. LibrePhotos requires 10 GB of HDD Space to operate through Docker Compose. Megan Crouse updated this article. source

While initial work to fix data problems should be expected before an AI project, ongoing repair of data problems taking hours of staff time per day can be a warning sign that the organization’s data wasn’t ready for AI, Erolin adds. Organizations ready for AI should be able to automate some of the data management work, he says. “If you’re spending so much time to keep the lights on for operational side of data and cleansing, then you’re not utilizing your domain experts for larger strategic tasks,” he says. The legacy problem Legacy systems that collect and store limited data are part of the problem, says Rupert Brown, CTO and founder of Evidology Systems, a compliance solutions provider. In some industries, companies are using legacy software and middleware that aren’t designed to collect, transmit, and store data in ways modern AI models need, he adds. source

By Emilie Ruscoe ( December 13, 2024, 8:47 PM EST) — Labaton Keller Sucharow LLP and Byrnes Keller Cromwell LLP will represent a proposed class of investors in litigation alleging software and data company ZoomInfo hurt investors after making missteps in an effort to retain new pandemic-era customers and ultimately writing down $33 million because of improperly recognized revenue…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

Let’s brace ourselves for a hard truth. Trust in marketing measurement is already poor, and left unchecked, it’s poised to get 20% worse. The pain that your organization feels surrounding B2B marketing measurement problems is real. Forrester’s Marketing Survey, 2024, revealed that 64% of B2B marketing leaders feel that their organization doesn’t trust measurement for decision-making. This hurts because marketing is a discipline with credibility that depends on data, facts, and insights, yet many marketing leaders don’t have faith in their company’s measurement when it matters the most. Not having trusted measurement hinders marketing’s ability to get its job done. Optimization of marketing efforts requires data to inform adjustments. That can’t happen when the metrics used to describe performance aren’t trusted. And without measurement to clearly depict marketing’s contribution, securing the budget and resources necessary to drive business impact becomes a losing battle. None of this is new, nor are the well-known contributors to the current state of marketing measurement (they include data quality, technology gaps, and the skills of measurement producers and consumers). All are aspects that B2B organizations continue to work to improve, but unfortunately, many B2B marketers will lose ground in these battles over the coming year. Why Is Measurement About To Get Tougher? We’re predicting that marketing measurement is about to get more difficult because of these compounding market forces: Buying complexity obscures so much. B2B sellers tell us that deal cycles have grown longer. Buyers tell us of the large quantities of individuals now involved in purchasing decisions. Persistent time-lag issues in detecting business impact grow more difficult with lengthened selling cycles. More people interacting with sales and marketing more times places more pressure on measurement systems already struggling to capture and make sense of their behaviors. Until vendor organizations recalibrate their business systems and processes, they will detect a limited portion of total buying interactions and will have to wait longer to understand results. Technology sprawl yields fractured data. The volume of technologies that make up the go-to-market technology stack results in disconnected data sources, and in turn, disconnected data is now a leading analytics challenge. Stitching together a cohesive picture of buyer behavior across these technologies is stretching the resources and skills of analytics teams — and this shows little signs of being alleviated. AI-inflated hope drives an expectation gap. AI has the potential to power meaningful improvements throughout B2B. This promise carries into widespread expectations that better measurement is possible by using AI to make sense of large volumes of data at speeds that humans will never replicate. But the distance between that vision and the current state of B2B marketing measurement should be counted in years, not months. B2B planning, processes and data aren’t yet in shape to meet AI’s potential. Stakeholders of all types will struggle for the foreseeable future to make sense of and develop faith in AI-driven views of performance. Expect a prolonged period of experimentation, missteps, and resets before B2B marketing analytics teams come anywhere close to cracking this code. Measurement can’t keep up with a renewed emphasis on reputation investment. B2B marketing investments have traditionally skewed toward capturing and advancing demand and so, too, has the focus of marketing measurement. But there’s growing recognition that demand efforts are not enough, and selling organizations must do more to influence buyers before they enter active buying cycles. Reputation spend now represents nearly one-quarter of marketing program investments, but we’re not seeing similar prioritization among what marketing leaders measure. Measurement analytics teams currently fall short in the skills and capabilities to measure this area that they’ve traditionally deprioritized. What’s To Be Done? Each of these market forces are larger than measurement, and there’s little that your analytics team can do to hold any of them at bay. What will separate the winning organizations from the rest is how they respond. In the face of these forces, here are a few actions that you can take to enhance your organization’s trust in marketing measurement: Tune your processes to buying complexity. Do the work to make it easier to link buyers to opportunity records, and work to capture not only self-guided interactions but personal ones, as well. Economize the B2B tech stack. Squeeze out duplicative capabilities found in best-in-breed solutions in favor of the broader solutions of platform providers. A more consolidated set of technologies will carry less overhead when it comes to data preparation and consolidation. Set clear reputation objectives. It’ll take time and resources to create comprehensive approaches to measuring reputation. In the meantime, start small by working with stakeholders to be sharp about setting reputation objectives and select a handful of available indicators that can show progress. Pair AI efforts with insight activation. Marketers are right to be excited by the potential of AI. At the same time, there’s a clear need to enable them to work more productively with the analytics already available. Marketing analytics teams need to redirect more of their time toward enabling their stakeholders to drive better results using existing resources. Doing so will better prepare them for the potential that AI is bound to unlock. Read our full Predictions 2025: B2B Marketing, Sales, And Product report to get more detail about how to get ahead in 2025. Set up a Forrester guidance session to discuss these predictions or plan out your 2025 B2B strategy. If you aren’t yet a client, you can download our complimentary B2B Predictions guide, which covers more of our top predictions for 2025. Find additional complimentary resources, including webinars, on the Predictions 2025 hub. Reserve your seat for the upcoming B2B Predictions webinars to hear more insights from Forrester analysts: source